Tracking and modeling non-rigid objects with rank constraints

By Lorenzo Torresani, Danny B. Yang, Eugene J. Alexander, C. Bregler. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001

308 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/85615a5f3604fbe571c8dcdb5701d9e1cc9d96b5

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In Aerospace Engineering

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Citations in the corpus

2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting (2007, methodology): “M̃2i+1 Qk M̃ 2 j+2 = 0 if i = k or i = j = k (25) M̃2i+2 Qk M̃ 2 j+1 = 0 if i = k or i = j = k (26) where i = 0, .”
Real-time combined 2D+3D active appearance models (2004, methodology): “This method was extended to a trilinear optimization approach in [11].”
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors (2008, methodology): “Various authors have described methods for learning linear subspace models with Principal Components Analysis (PCA) for recognition, tracking, and reconstruction [4], [9], [24], [31].”
Simultaneous Localization, Mapping and Moving Object Tracking (2007, methodology): “The ideas based on the factorization techniques and the shape basis representation are presented in Bregler et al. (2000), Brand (2001), Torresani et al. (2001) and Xiao et al. (2004), where different constraints are used for finding the solution.”
Approximate Nearest Subspace Search with Applications to Pattern Recognition (2007, background): “In computer vision, for example, sub-spaces are often used to capture the appearance of objects under different lighting [4, 18], viewpoint [21, 19], articulation [7, 20], and even identity [3, 5].”
Face transfer with multilinear models (2005, methodology): “These techniques estimate either linear coefficients for known basis shapes [Bascle and Blake 1998; Brand and Bhotika 2001] or both the basis shapes and the coefficients, simultaneously [Bregler et al. 2000; Torresani et al. 2001].”
Analyzing 3D Objects in Cluttered Images (2012, methodology): “To estimate such models from 2D data, we adapt methods designed for tracking morphable shapes to 3D object category recognition [29] [28] .”
Model evolution: An incremental approach to non-rigid structure from motion (2010, background): “[5], a number of excellent solutions [3, 12, 10, 9, 4, 14, 15, 11, 13, 2] have been proposed to solve NRSFM in the past decade.”